{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:2KP6FNVXVVYRZQKMH3EMSNCKK6","short_pith_number":"pith:2KP6FNVX","canonical_record":{"source":{"id":"2111.08635","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2021-11-16T17:25:05Z","cross_cats_sorted":["cs.LG","cs.SD"],"title_canon_sha256":"00b8ccfd8cad3cf5b22007bef5c5105639a7c0e30d7848fd083140befddd1cf2","abstract_canon_sha256":"9bfad77ce7fa7cd408bf90ff7784af3a7a5aaba9ca387b5f9f0a0dfc319a9a14"},"schema_version":"1.0"},"canonical_sha256":"d29fe2b6b7ad711cc14c3ec8c9344a57bd71a5a6c93a3233dfb1f88c8bcc23fd","source":{"kind":"arxiv","id":"2111.08635","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.08635","created_at":"2026-07-05T03:32:36Z"},{"alias_kind":"arxiv_version","alias_value":"2111.08635v1","created_at":"2026-07-05T03:32:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.08635","created_at":"2026-07-05T03:32:36Z"},{"alias_kind":"pith_short_12","alias_value":"2KP6FNVXVVYR","created_at":"2026-07-05T03:32:36Z"},{"alias_kind":"pith_short_16","alias_value":"2KP6FNVXVVYRZQKM","created_at":"2026-07-05T03:32:36Z"},{"alias_kind":"pith_short_8","alias_value":"2KP6FNVX","created_at":"2026-07-05T03:32:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:2KP6FNVXVVYRZQKMH3EMSNCKK6","target":"record","payload":{"canonical_record":{"source":{"id":"2111.08635","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2021-11-16T17:25:05Z","cross_cats_sorted":["cs.LG","cs.SD"],"title_canon_sha256":"00b8ccfd8cad3cf5b22007bef5c5105639a7c0e30d7848fd083140befddd1cf2","abstract_canon_sha256":"9bfad77ce7fa7cd408bf90ff7784af3a7a5aaba9ca387b5f9f0a0dfc319a9a14"},"schema_version":"1.0"},"canonical_sha256":"d29fe2b6b7ad711cc14c3ec8c9344a57bd71a5a6c93a3233dfb1f88c8bcc23fd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:32:36.092837Z","signature_b64":"07Qkeyz4Fg7BE88arWXK5CxNR6TGyGdRuIV9U7V25/1UeUEuljgF5C3WNH3+wt6IbgEWWeOG2kzCnxvso/RvDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d29fe2b6b7ad711cc14c3ec8c9344a57bd71a5a6c93a3233dfb1f88c8bcc23fd","last_reissued_at":"2026-07-05T03:32:36.092405Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:32:36.092405Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2111.08635","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:32:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GDh0w0k95+SbsiWPhx2AZrbL2mS+25vkv+inQfVE0EO87tsx0ydVxuYqXlN7XZ50kJzWvp6UhALu+w9PnZtZCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T10:53:41.018949Z"},"content_sha256":"4d985a59a9f12e28a6553317b3df2968bdabec7ea23178761f532da9cfc9af22","schema_version":"1.0","event_id":"sha256:4d985a59a9f12e28a6553317b3df2968bdabec7ea23178761f532da9cfc9af22"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:2KP6FNVXVVYRZQKMH3EMSNCKK6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Single-channel speech separation using Soft-minimum Permutation Invariant Training","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SD"],"primary_cat":"eess.AS","authors_text":"John H.L. Hansen, Midia Yousefi","submitted_at":"2021-11-16T17:25:05Z","abstract_excerpt":"The goal of speech separation is to extract multiple speech sources from a single microphone recording. Recently, with the advancement of deep learning and availability of large datasets, speech separation has been formulated as a supervised learning problem. These approaches aim to learn discriminative patterns of speech, speakers, and background noise using a supervised learning algorithm, typically a deep neural network. A long-lasting problem in supervised speech separation is finding the correct label for each separated speech signal, referred to as label permutation ambiguity. Permutatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.08635","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2111.08635/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:32:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wTfUWdhhIwKluILExAV+sGHOjOuagfdoJeIhfjYdoDa7JygCevkuDe3QF4atki8uSbf+3btyUMsSxCjGTgzRBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T10:53:41.019479Z"},"content_sha256":"69f6a04e6ce77600466744c03edf8be782dd549a1783f9c6b90b5c1ff3b92332","schema_version":"1.0","event_id":"sha256:69f6a04e6ce77600466744c03edf8be782dd549a1783f9c6b90b5c1ff3b92332"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2KP6FNVXVVYRZQKMH3EMSNCKK6/bundle.json","state_url":"https://pith.science/pith/2KP6FNVXVVYRZQKMH3EMSNCKK6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2KP6FNVXVVYRZQKMH3EMSNCKK6/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-23T10:53:41Z","links":{"resolver":"https://pith.science/pith/2KP6FNVXVVYRZQKMH3EMSNCKK6","bundle":"https://pith.science/pith/2KP6FNVXVVYRZQKMH3EMSNCKK6/bundle.json","state":"https://pith.science/pith/2KP6FNVXVVYRZQKMH3EMSNCKK6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2KP6FNVXVVYRZQKMH3EMSNCKK6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:2KP6FNVXVVYRZQKMH3EMSNCKK6","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9bfad77ce7fa7cd408bf90ff7784af3a7a5aaba9ca387b5f9f0a0dfc319a9a14","cross_cats_sorted":["cs.LG","cs.SD"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2021-11-16T17:25:05Z","title_canon_sha256":"00b8ccfd8cad3cf5b22007bef5c5105639a7c0e30d7848fd083140befddd1cf2"},"schema_version":"1.0","source":{"id":"2111.08635","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2111.08635","created_at":"2026-07-05T03:32:36Z"},{"alias_kind":"arxiv_version","alias_value":"2111.08635v1","created_at":"2026-07-05T03:32:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.08635","created_at":"2026-07-05T03:32:36Z"},{"alias_kind":"pith_short_12","alias_value":"2KP6FNVXVVYR","created_at":"2026-07-05T03:32:36Z"},{"alias_kind":"pith_short_16","alias_value":"2KP6FNVXVVYRZQKM","created_at":"2026-07-05T03:32:36Z"},{"alias_kind":"pith_short_8","alias_value":"2KP6FNVX","created_at":"2026-07-05T03:32:36Z"}],"graph_snapshots":[{"event_id":"sha256:69f6a04e6ce77600466744c03edf8be782dd549a1783f9c6b90b5c1ff3b92332","target":"graph","created_at":"2026-07-05T03:32:36Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2111.08635/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The goal of speech separation is to extract multiple speech sources from a single microphone recording. Recently, with the advancement of deep learning and availability of large datasets, speech separation has been formulated as a supervised learning problem. These approaches aim to learn discriminative patterns of speech, speakers, and background noise using a supervised learning algorithm, typically a deep neural network. A long-lasting problem in supervised speech separation is finding the correct label for each separated speech signal, referred to as label permutation ambiguity. Permutatio","authors_text":"John H.L. Hansen, Midia Yousefi","cross_cats":["cs.LG","cs.SD"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2021-11-16T17:25:05Z","title":"Single-channel speech separation using Soft-minimum Permutation Invariant Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.08635","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4d985a59a9f12e28a6553317b3df2968bdabec7ea23178761f532da9cfc9af22","target":"record","created_at":"2026-07-05T03:32:36Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9bfad77ce7fa7cd408bf90ff7784af3a7a5aaba9ca387b5f9f0a0dfc319a9a14","cross_cats_sorted":["cs.LG","cs.SD"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2021-11-16T17:25:05Z","title_canon_sha256":"00b8ccfd8cad3cf5b22007bef5c5105639a7c0e30d7848fd083140befddd1cf2"},"schema_version":"1.0","source":{"id":"2111.08635","kind":"arxiv","version":1}},"canonical_sha256":"d29fe2b6b7ad711cc14c3ec8c9344a57bd71a5a6c93a3233dfb1f88c8bcc23fd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d29fe2b6b7ad711cc14c3ec8c9344a57bd71a5a6c93a3233dfb1f88c8bcc23fd","first_computed_at":"2026-07-05T03:32:36.092405Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:32:36.092405Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"07Qkeyz4Fg7BE88arWXK5CxNR6TGyGdRuIV9U7V25/1UeUEuljgF5C3WNH3+wt6IbgEWWeOG2kzCnxvso/RvDA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:32:36.092837Z","signed_message":"canonical_sha256_bytes"},"source_id":"2111.08635","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4d985a59a9f12e28a6553317b3df2968bdabec7ea23178761f532da9cfc9af22","sha256:69f6a04e6ce77600466744c03edf8be782dd549a1783f9c6b90b5c1ff3b92332"],"state_sha256":"41c68e522567fdffc21d2cdebfd2937f862f9faa6ed5ba6fc66fd508e703e33e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ObPYPZwl0sU/AeMdeq+FKe+lu0VkI+oMUUr0o5+qj5x2XWi/4yoticy/pMutOsmPLG6aVQK38WMRevLOhPNuBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T10:53:41.025214Z","bundle_sha256":"07b7b110ae7839f7147aeaa1c67634b1f6366de369e4db023e1682b402c7648f"}}